Field
The present disclosure relates generally to oil and gas data analytics, and more specifically, to generating decision cubes from cross dependent data sets in oil and gas data sets.
Related Art
In the related art, oil and gas rigs utilize computerized systems to assist the operators of the rigs throughout the different phases of the oil or gas rigs (e.g., exploration, drilling, production, completions, etc.). Such computer systems are deployed for the development of energy sources such as shale gas, oil sands, and deep water resources. In the related art, attention has shifted to the development of shale gas for supplying future energy needs. Related art advances in horizontal directional drilling and hydraulic fracturing technologies have unlocked the potential for recovering natural gas from shale to become a viable energy source.
However, the issue of maximizing output from an oil and gas reservoir, particularly shale gas reservoirs, is not well understood, even with the assistance from present computer systems. The process of making production decisions and sizing top-side facilities is mostly a manual process that depends on the judgment of the rig operator. Furthermore, operators often struggle with real time performance of support for down-hole gauges, semi-submersible pumps, and other equipment. Non-Productive Time (NPT) for a rig may constitute over 30% of the cost of drilling operations.
One aspect of the issue of output maximization is the lack of effective data processing and data analytics, along with the sheer volume of data received from oil and gas wellsites. The data sets obtained from different upstream processes can be substantial in terms of number of available attributes. Manually developing applications that utilize these attributes can be very time-consuming.